The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle’s performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.
Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles
2022 IEEE Intelligent Vehicles Symposium (IV) ; 1005-1010
2022-06-05
2255371 byte
Conference paper
Electronic Resource
English
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SAE Technical Papers | 2022
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